How to Orchestrate a Multi-Player Agentic Product Operating Model

How do you run your product operating model as agents show up in every part of the business? How do data, context, source of truth, and inference interconnect? And how do you drive better outcomes while keeping token and inference costs low?

Join Becky Flint, Founder & CEO of Dragonboat, as she unpacks what a multi-player agentic product operating model looks like in practice, and shares real use cases and best practices for orchestrating decisions across different roles of humans and agents. 

In this session, Becky walks through:

• How humans and agents work together to reason and act across the PDLC, driving efficiency and better outcomes while keeping token and inference costs low
• Agentic PDLC best practices, from competitive research to idea to spec-to-code and launch to market
• MCP-powered orchestration across the tools your teams (and their agents) already use
• The context that makes it work: how a shared source of truth, memory, and consistent meaning come together so people and agents operate on the same product operating reality

Key Takeaways

Becky Flint, Founder & CEO of Dragonboat, walked through what it actually takes to orchestrate humans and agents across the product development lifecycle, from personal AI use to full agentic orchestration. Here are the key takeaways from the session:

• There are three stages of multi-agent work: personal/free agents, AI teammates, and agentic orchestration. Becky described free agents as the early stage everyone already uses for productivity, AI teammates as more skilled but often blocked by cross-functional dependencies, and agentic orchestration as the stage where agents and humans across multiple teams work together.
• Without a trusted source, agents spend most of their effort inferring instead of acting. Becky’s example: a team using an agent to scan emails, meeting notes, and Jira tickets just to produce a roadmap status update — expensive, slow, and still not fully accurate. With a reliable source of truth in place, that inference happens once instead of every time.
• Dragonboat’s MCP gives agents direct access to data that’s already calculated, not raw data to figure out. In the demo, Becky showed an agent in Claude pulling roadmap progress through Dragonboat’s MCP server, progress and ownership already aggregated, with the ability to drill into each item to validate it.
• Semantics need to be unified before agents can reason across teams. Becky’s example: the same customer showing up as “Acme” in one system and “Acme Corp” in another. Unifying that into one entity is what lets an agent correctly find and connect information across tools.
• An ontology is more than a data graph, it’s the rules and actions that govern how agents behave. Becky was explicit that ontology isn’t just data or linkage; it includes things like what counts as strategic drift, what a dependency means beyond “A blocks B,” and the governance rules for who or what can read and write to which data.

“Think about your AI agent as your partner, as part of your team, an extension of you. Agents operate in the digital version of your real world of operating, and the ontology is designed to reflect that — it’s a digital version of your actual reality of how you operate. That’s why you need an ontology: so agents can work the same way you want them to work, and not go rogue.”

 

— Becky Flint, Founder & CEO, Dragonboat

Want to go deeper?

• Read the foundation behind the session: What is Product Operating Ontology
• See Dragonboat MCP in action: Agentic workflows with Dragonboat MCP and AI Connectors
• Ready to put this into practice? Schedule a Demo